The Apache Flink course offers comprehensive training for learners aiming to master stream processing and real-time data analytics. It is designed to help participants gain a solid foundation in Apache Flink, equipping them with the skills needed to build scalable stream processing applications. Apache Flink is one of the latest and successful platforms that industries are searching to use because of its even time capacities, which make a great advantage for the business. Through Apache Flink Online Training, you will acquire thorough knowledge about Apache Flink in a detailed manner, which will provide you a bright path for a flourishing career in Big Data Streaming Engine. This Apache Flink course is beneficial for data engineers, data scientists, and developers interested in real-time data processing and analytics.
Course Objectives:
In this course, you will learn to:
- Become adept in latest Big Data technology – ‘Apache Flink’ and be future ready
- Master core concepts of Apache Flink internals
- Learn Data Streaming Architecture with Apache Flink
- Learn how to handle diverse workload with Apache Flink
- Setup of multi-node Flink cluster on cloud
- Gain in depth knowledge on Flink Stack
- Grasp fundamental concepts of Flink Libraries and APIs
- Learn development of real-time streaming Flink applications
- Handle structured data using Table-API
- Perform advanced analytics using Flink ML – Flink Machine learning Example
- Process graph at Scala using Gelly – Flink Graph API
- Complex Event Processing using Apache Flink – Flink CEP
- Integrate with different Big Data systems
- Work on real-time Flink Project
Course content
Demystify Scala
- Introduction to Scala
- Setup, Installation and configuration of Scala
- Develop and execute Scala Programs
- Scala operators and features
- Different Functions, procedures and Anonymous functions
- Deep dive into Scala APIs
- Collections Array, Map, Lists, Tuples and Loops
- Advanced operations – Pattern matching
- Eclipse IDE with Scala
Object Oriented and Functional Programming
- Object oriented programming
- Oops concepts
- Constructor, getter, setter, singleton, overloading and overriding
- Type Inference, Implicit Parameters, Closures
- Lists, Maps and Map Operations
- Nested Classes, Visibility Rules
- Functional Structures
- Functional programming constructs
Introduction to Apache Flink
- Learn What and why Apache Flink
- Understand Features of Apache Flink
- Apache Flink architecture and Flink design principles
- Work of master process – JobManager
- Role of worker process – TaskManager
- Workers, Slots and Resources
- Overview of Apache Flink APIs
- Understand difference between Apache Spark and Apache Flink to learn Flink vs Spark.
Master Flink Stack
- Distributed Streaming DataFlow at Runtime with Flink
- Apache Flink APIs
- Apache Flink Libraries
- Data Flow in Apache Flink
- Fault tolerance in Apache Flink
Setup and Installation of single node Flink
- Setup of Apache Flink environment and pre-requisites
- Installation and configuration of Flink on single node
- Troubleshooting the encountered problems
Setup and Installation of multi node Flink cluster and Cloud
- Setup environment on Cloud
- Install pre-requisites on all nodes
- Deploy Apache Flink on cluster and Cloud
- Play with Flink in cluster mode
Master DataStream API for Unbounded Streams
- Introduction to Flink DataStream API
- Different DataStream Transformations in Flink
- Various Data Sources – File based, Socket based, Collection based, Custom
- Responsibility of Data Sink in Apache Flink
- Iterations in DataStream APIs
- DataStream Execution Parameters – Fault tolerance, Controlling Latency
Learn Flink DataSet APIs for Static Data
- Overview of DataSet APIs in Flink
- Various DataSet Transformations in Flink
- Different Data Sources – File based, Collection based, Generic
- Responsibility of Data Sink in Flink DataSet APIs
- Iteration Operators in DataSet APIs
- Operating on Data Objects in Functions – Object Reuse Disabled/Enabled
Play with Flink Table APIs and SQL Beta
- Registering Tables in Flink
- Table Access and various Table API operators in Flink
- SQL on batch tables and Streaming Tables
- Writing Flink Tables to external sinks
Windows in Flink
- Introduction to Windowing
- Window Assigners
- Various Time Notions of Windows in Flink
- Tumbling Windows Implementation
- Sliding Windows Implementation
- Session Windows Implementation
- Global Windows Implementation
- Triggers & Evictors
- Triggers in Windows
- Evictors for Windows
Watermarks and Late elements
- Watermarks, Late Elements & Allowed Lateness
- How to generate Watermarks
State, Checkpointing and Fault tolerance
- What is a State in Flink
- Checkpointing and Barrier Snapshoting
- Incremental Checkpointing (New Feature)
- Types of States
- Value State Implementation
- List State Implementation
- Reducing State Implementation
- Managed Operator State Implementation
- Implement Checkpointing in a Flink Program
- The Broadcast State Implementation
- Queryable State (Beta Version)
Interacting with Real-Time Data
- Getting Twitter data using its APIs
- Adding Kafka to Flink as a Data source
- Solve Real-Time Case studies using Flink
- Twitter data analysis using Flink
- Bank Real-Time Fraud detection
- Stock Real-Time Data Processing
Table & SQL API | Relational APIs of Flink
- Introduction to Table & SQL API
- The Flink Table DSL
- Flink SQL like syntax
- How to register a Table in Relational APIs
- Writing Queries in Table & SQL API
Gelly API for Graph Processing
- Calculate Friends of Friends of a Person using GELLY API
- What is a Graph
Apache Flink Libraries
- Overview of Flink Libraries
- Flink CEP – Complex Event Processing library
- Apache Flink Machine Learning library
- Apache Flink Gelly -Graph processing API and Library
Flink Integration with other Big data tools
- Integrate Flink with Hadoop
- Process existing HDFS data with Flink
- Yarn and Flink integration
- Flink Data Streaming with Kafka
- Consume data in real time from Kafka
Programming in Flink
- Parallel Data Flow in Flink
- Develop complex Streaming applications in Flink
- Handle Batch processing in Flink using DataSet APIs
- Troubleshooting and Debugging Flink Programs
- Best Practices of development in Flink
- Real time Apache Flink Project
Integrating Flink with other Big Data projects
- Hadoop
- YARN
- Kafka
To see the full course content Download now
Course Prerequisites
- Prior knowledge of distributed frameworks and the OOPS concept will be helpful but not compulsory
- Basic Knowledge of Java or Scala Programming
- Understanding of Data Processing Concepts:
- Experience with Distributed Systems (Optional but Beneficial)
- Fundamentals of Linux Operating System
- Elementary Knowledge of Database Systems
- General Development Environment Setup
Who can attend
This course is ideal for:
- Hadoop Developers
- Spark Developers
- ETL developers
- Analytics professionals and Architects
- Big Data architects, engineers, and developers
- Software engineers who aspire to get into the field of Hadoop and Spark
- Big Data professionals wishing to expedite their career as Flink developer
- Database Administrators looking to integrate Flink into their data systems
- Technical Project Managers responsible for data processing projects
- Business Intelligence Professionals looking to expand their skillset into real-time analytics
- DevOps Engineers involved in deploying and managing data-intensive applications
- Freshers who desire to learn Apache Flink Stream processing framework
Number of Hours: 30hrs
Certification
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- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
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